Soil quality index assessment for conventional, organic and INM based rice cropping systems using key indicators as influenced by imbalanced fertilization

Introduction: Imbalanced fertilization in rice ecosystems disrupts nutrient equilibrium between soil replenishment and crop uptake, leading to reduced yield and soil degradation. Sustainable alternatives such as organic and Integrated Nutrient Management (INM) practices are increasingly evaluated for their potential to enhance soil quality and productivity.

Methods: A four-season study compared conventional farming (CF), organic farming (OF), and INM practices in rice systems. Principal Component Analysis (PCA) was used to eliminate multicollinearity and derive relative weights (Wi) for selected soil indicators (Si) to compute the soil quality index (SQI). Key biological and chemical indicators—phosphatase activity (PA), water-holding capacity (WHC), soil microbial biomass carbon (SMB-C), organic carbon (OC), zinc (Zn), and urease activity (UA)—were measured.

Results: CF fields recorded lower SQI values compared with organic and INM systems. Organic and INM fields exhibited higher SQI values of 0.99 and 0.88, respectively. Within CF treatments, a super-optimal nitrogen dose (250%) resulted in the lowest SQI (0.573) and yield (3.20 t ha⁻¹), while the 125% N treatment (CF6) achieved the highest SQI (0.715) and yield (6.20 t ha⁻¹). Super-optimal phosphorus and potassium levels generally reduced yield except in CF6.

Discussion/Conclusion: Integrating soil physical, chemical, and biological properties through a weighted additive index (WAI) method effectively established the link between soil quality and function. Optimizing fertilizer doses rather than maximizing them can improve both soil quality and rice productivity, offering a sustainable pathway for managing nutrient dynamics in intensive rice ecosystems.

Introduction

Soil quality waning posed a remarkable challenge for increasing the economic growth, agricultural productivity and to attain a healthy environment (Xing et al., 2024). Across many regions, declining soil quality has emerged as a significant challenge for enhancing crop yields and ensuring sustainable land use. This degradation is primarily driven by inappropriate soil and land management practices (Abdullahi et al., 2023). Under natural conditions, pedogenic processes maintain the equilibrium of soil properties. However, anthropogenic interventions, including intensive cultivation and imbalanced fertilization, disrupt this balance, leading to nutrient depletion, reduced microbial activity, and diminished soil functionality (Lal et al., 2020; Tate, 2020; Yu et al., 2020).

Sustainable land use management, therefore, depends on informed decision-making, which requires an understanding of key soil quality indicators such as soil organic matter, nutrient availability, microbial biomass, and soil enzyme activity are closely linked to crop productivity and soil quality (Nziguheba et al., 2022; Azadi et al., 2021; Kolapo et al., 2022). Soil quality is defined as the suitability of a specific kind of soil to function within its capacity in natural or managed ecosystems, sustaining plant and animal productivity, maintaining or enhancing water and air quality, and supporting human health and habitation (Delgado and Gómez, 2024). Soil Quality Index (SQI) is a composite indicator that integrates various physical, chemical, and biological properties of soil into a single quantitative value to evaluate overall soil quality and functionality. It reflects the soil’s ability to perform key ecological functions such as nutrient cycling, water retention, and support of plant growth and serves as a valuable diagnostic tool to compare management practices, monitor temporal changes, and guide sustainable land-use decisions (Zhang X. et al., 2022). When integrated into SQI, these indicators provide a holistic measure of soil capacity to sustain crop production over time (Chaudhry et al., 2024; Ghorai et al., 2023). Consequently, systematic assessment of soil degradation using SQI is essential to identify priority areas for targeted soil management and interventions (Gao et al., 2024; Zahedifar, 2023; Choudhary et al., 2019).

One of the primary factors affecting soil quality is the imbalanced application of inorganic fertilizers. Applying nutrients either above or below recommended levels can lead to adverse effects on soil properties, crop productivity, and long-term soil quality (Liu et al., 2017; Bai et al., 2018; Bora, 2022; Saha et al., 2024). In India, the N:P:K consumption ratio has shifted from 8.9:2.2:1 in 1960 to 6.7:2.7:1 in 2023, deviating from the recommended 4:2:1 ratio (FAI, 2023; Department of Agriculture and Farmers Welfare). Although improvements have been made, reaching 4.3:2:1 in 2009–2010 (Wu et al., 2018; Guo et al., 2019; Meyer et al., 2020), imbalances remain a serious concern in several states (Bhattacharyya et al., 2015; Eo and Park, 2016; Padhan et al., 2020). Such practices reduce nutrient-use efficiency, threaten soil quality, and create three major adverse impacts: wastage of resources without yield gains, negative effects on soil properties, and long-term deterioration of soil quality (Prakash, 2023; Bhattacharyya et al., 2015; Bhatt M. et al., 2019).

At the farm level, fertilizer use is frequently determined by availability and cost rather than by soil testing or expert recommendations (Sharma and Singhvi, 2017; Savari and Gharechaee, 2020; Gars et al., 2025). Consequently, the current NPK usage ratio of 8.9:2.2:1.0 significantly deviates from the recommended 4:2:1 ratio (Ding et al., 2018; Wang et al., 2018; Ichami et al., 2019; Shah et al., 2025; Shah and Wu, 2019; Rawal et al., 2025). Continuation of such erratic fertilizer practices over time results in depletion of native soil nutrient resources and reduces the soil’s quality to sustain high production levels in the future (Chen et al., 2019b; Manna et al., 2005). Fertilizer usage scenarios can be broadly categorized based on recommended nutrient application standards (ICAR, 2017; FAO, 2019). Situation A represents suboptimal application of all three major nutrients, with nitrogen deviating 6.6 times from the recommended norm. Situation B represents fivefold deviation in the NPK ratio, with excessive nitrogen application (Reynier, 2025). Situation B requires careful restriction of nutrient use to avoid unnecessary expenditure, detrimental effects on soil properties, and long-term soil degradation. (Mishra, 2025; Singh et al., 2024; Assefa and Hans-Rudolf, 2016; Massah and Azadegan, 2016). These variations in nutrient application highlight the critical need for studies that integrate fertilizer use patterns with SQI to assess impacts on soil quality and sustainability, which remain poorly addressed in existing research.

Assessment of soil quality using SQI, although challenged by variability in dataset selection, scoring, and calculation methods, remains an effective tool for identifying areas requiring careful soil management (Sanad et al., 2024; Klimkowicz-Pawlas et al., 2019; Lin et al., 2019). Statistical approaches such as regression analysis and principal component analysis (PCA) or expert opinion are commonly used for indicator selection. Scoring functions, whether linear or nonlinear, convert indicator values into comparable scores (Nabiollahi et al., 2017; Juhos et al., 2019; Lin et al., 2019). While different methods may yield variations in SQI values for the same location, the index continues to provide a valuable framework for targeted soil quality assessment (Ellur et al., 2024; Thakur et al., 2022; Mishra et al., 2017; Raiesi, 2017). However, there remains no globally standardized protocol for selecting indicators, applying scoring methods, and calculating SQI, particularly in rice ecosystems under varying fertilizer regimes, which limits comparability and adoption of SQI-guided management practices.

Previous studies in rice and sugarcane systems highlight the effectiveness of SQI in assessing soil physical, chemical, and biological properties. For example, soils in Thanjavur and Tiruvarur districts showed SQI values ranging from 0.72 to 0.85, indicating good soil quality (Dharumarajan et al., 2024). In rice-wheat systems, integrated nutrient management produced SQI values of 0.59–0.63, with strong correlations (R² = 0.928) between SQI and yield (Rahmanipour et al., 2014). In subtropical rice-based systems, attributes such as soil organic carbon (SOC), labile carbon, dehydrogenase activity (DHA), and cation exchange capacity (CEC) were strongly linked with productivity (Paul et al., 2019). Similarly, available potassium (K), total nitrogen (TN), and soil organic matter (SOM) were identified as key determinants of soil quality across multiple sites (Dhaliwal et al., 2019). Despite these insights, there is limited research directly linking fertilizer imbalances, specific nutrient management scenarios, and SQI across diverse rice ecosystems, particularly in South India.

Rice (Oryza sativa) is a staple crop cultivated on approximately 158 million hectares globally, with China and India contributing around 55% of global production (FAO, 2023). In Tamil Nadu, rice is mainly grown under wetland conditions, particularly in Anaimalai and Thondamuthur blocks of Coimbatore district covering 1,500 ha and 200 ha, producing 6,450 MT and 740 MT at average productivities of 4.3 t ha⁻¹ and 3.7 t ha⁻¹, respectively (Agricultural Engineering Department, 2017). Being a nutrient-intensive crop, rice heavily depends on soil quality. Excessive fertilizer application often reduces nutrient-use efficiency due to losses through runoff, volatilization, and leaching, thereby stagnating yields despite higher inputs (Penn and Camberato, 2019; Yadav et al., 2023; Nakachew et al., 2024).

The present study aims to evaluate the impact of imbalanced fertilizer application on soil physical, chemical, and biological properties in rice ecosystems. It is hypothesized that excessive or imbalanced use of fertilizers adversely affects soil quality, and that the Soil Quality Index (SQI) can serve as an effective tool to quantify these effects and identify critical soil management interventions. By integrating soil properties with SQI analysis, this study provides a systematic framework to understand how fertilizer management practices influence soil quality, crop productivity, and long-term ecosystem sustainability in rice-based systems.

Materials and methods

Study site

The study was conducted in CF, OF and INM based rice fields in Thondamuthur block, Coimbatore district (10.9899°N and 76.8409°E). Of which, ten fields were having conventional farming practices, one field each was organic based and INM based to evaluate and illustrate the changes taking place due to the added nutrient inputs in three ecosystems (Table 1). The average temperature and rainfall of the region is 28.3 °C and 641 mm respectively. Soil types distributed in the area is red calcareous, red non-calcareous and black soil. Soil series viz., palathurai and somayanur series are the two-soil series located in the block. Predominant soil type is clay loam which comes under the palathurai series. The fields were monitored periodically for the dosage of fertilizers applied and crop grown soils were tested for physical, chemical and biological properties for four seasons in order to develop the SQI. Yield parameters were also observed during four seasons of study (2019–2023).

Table 1

Table 1. List of selected rice farm with cropping pattern, soil texture and series and N:P:K ratio.

Soil sampling and laboratory measurements

In each field, three replicate composite soil samples were collected from the 0–15 cm depth during each of the four cropping seasons. Sampling points were randomly selected within the transect while avoiding field boundaries and visibly disturbed areas (Figure 1). The fields were monitored periodically for fertilizer dosage, crop management practices, and yield performance. The collected soil samples were air dried, sieved through 2 mm sieve and preserved for further analysis. The soil parameters studied were bulk density (BD), WHC, pH, EC, soil organic carbon (SOC), available NPK, zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), SMB-C, soil microbial biomass nitrogen (SMB-N), soil microbial biomass phosphorus (SMB-P), UA, PHOS and DHA activity. Standard methods were followed for analyzing the properties of the soil.

Figure 1

Figure 1. Study area showing the Conventional farming (CFs), OF and INM field in Thondamuthur Block.

Soil pH was determined by using mixture of soil sample into deionized water (1:2.5, w/v) using ELICO–LI615 pH meter (Jackson, 1973). Salinity was determined by measuring the EC of the saturated soil extract using ELICO CM 180 Conductivity meter (Jackson, 1973). The determination of soil BD was performed by core method (Gupta, 1980). Soil WHC was measured by pressure plate apparatus (Richards, 1965). SOC was estimated by chromic acid wet digestion method (Walkley and Black, 1934). Soil available NPK was estimated by alkaline permanganate method (Subbiah and Asija, 1956), Olsen and Bray (Olsen et al., 1954) and Neutral normal ammonium acetate using flame photometer method respectively (Stanford and English, 1949) respectively. DTPA (Diethylene Triamine Penta acetic acid) extraction method was used for the determination of micronutrients viz., zinc (Zn), iron (Fe), copper (Cu) and manganese (Mn) (Jackson, 1973). UA was determined following the method of Tabatabai and Bremner (1972), based on the quantification of ammonium released from urea hydrolysis. PA was assayed according to Tabatabai and Bremner (1969) using p-nitrophenyl phosphate as substrate, and expressed as µg p-nitrophenol released g^-1 soil h^-1. DHA activity was analyzed by the Casida et al. (1964) method, involving the reduction of 2,3,5-triphenyltetrazolium chloride (TTC) to triphenyl formazan (TPF). All enzyme activities were expressed on an oven-dry soil weight basis. Chloroform-fumigation extraction method was performed for the determination of SMB-C, SMB-N and SMB-P (Jenkinson and Powlson, 1976).

Soil quality index computation methodology

Assessment of soil quality indices was attempted as described by Andrews et al. (2002) which consist of following steps i. Selecting the minimum data set (MDS) of indicators which should represent the best soil function related to soil quality using PCA (principal component analysis). ii. Based on their performance of indicator, scoring was given 0 to 1 scale. iii. SQI was developed by integrating the weighed and additive approaches.

Selecting the MDS (minimum data set)

Using the SPSS 16.0 (Statistical Package for the Social Sciences) software program, principal component analysis (PCA) was used to choose the indicators. Linear combinations of variables that account for maximum variance within the set by describing vectors of closest fit to the n observations in p dimensional space, subject to being orthogonal to one another is the definition of a dataset’s principal components. Reducing the data’s dimensions while minimising information loss was the PCA’s goal. The original data-set’s principal components are uncorrelated, contain contributions from all variables, and are arranged so that the first few keep the majority of the variation. Each primary component’s eigen value indicates how variable it is in relation to the overall variance.

Principal component analysis

Before performing PCA, data normality was assessed using the Shapiro–Wilk test (Shapiro & Wilk, 1965). Pearson correlation coefficients were calculated to examine the relationships among soil parameters. The suitability of the dataset for PCA was evaluated using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity (Kaiser, 1974; Bartlett, 1954). Only variables meeting these criteria were included in the PCA, ensuring reliable identification of the principal components.

PCA was performed on eighteen soil physical, chemical, and biological parameters. The factors that all have high factor loading and eigen values were thought to be the greatest indicators of the changes in soil quality brought about by the cropping sequence. Brejda et al. (2000) state that as a result, the component with an eigen value >1 and at least 5% of the variation was chosen. The factors from each PC that had an absolute value within ten percent of the maximum loading factor were kept in the MDS, which is regarded as a weighed factor. In cases where many factors were maintained in a single PC correlation, duplicate variables were eliminated from the MDS (Andrews et al., 2002). All of the factors that were either not correlated or not significant with one another were kept after the well-correlated variables were excluded. The weighed factor value for the indicators selected under the PC was obtained by dividing the variation within each PC that should account for a given level of variance by the total variation across all PCs with eigen values greater than one.

Linear scoring technique

Using the linear scoring method, a subset of the MDS’s indicators were scored into dimension-less values between 0 and 1 (Kour et al., 2023). Two soil functions were used to define and categorise indicators. When the chosen indicators were in decreasing order, they were deemed negative (i.e., less is better) and excellent (i.e., more is better) when they were in increasing order in the MDS. Each observation was divided by the greatest observed value for “more is better” indications, resulting in a score of 1 for the highest observed value. In order to determine the lowest observed value for “less is better” indications, each observation was divided by the lowest observed value, resulting in a score of 1. The following linear curves were subsequently used as “more is better” Equation 1 or less is better Equation 2 scoring functions:

$\mathrm{L}\left(\mathrm{Y}\right)=\frac{\mathrm{X}}{\mathrm{\text{Xmax}}}(1)$

$\mathrm{L}\left(\mathrm{Y}\right)=\frac{\mathrm{\text{Xmin}}}{\mathrm{X}}(2)$

Where L (Y) is the linear score varying from 0 to 1, x is the soil indicator value and Xmax and Xmin are the maximum and minimum value of each soil indicator observed between the two land uses.

Non linear scoring method

This scoring system uses the optimum is good (pH) in addition to the good is better and less is better indications. Equation 3 was applied in this manner to score the indication. 1 was regarded as (a) universally. Slope (b) of this equation was, according to Bastida et al. (2006), −2.5 for better is better and 2.5 for less is better.

$\text{NL\hspace{0.17em}}\left(\mathrm{Y}\right)=\frac{\mathrm{a}}{\left(\mathrm{1}+\left(\frac{\mathrm{X}}{\mathrm{X}\mathrm{0}}\right)\mathrm{b}\right)}(3)$

SQI computation

Soil samples collected across twelve rice fields served as the basis for calculating the SQI. The WAI method was applied, combining the selected indicators using the following equation to derive an overall SQI as the final step of the assessment (Andrews et al., 2002).

$\text{SQI}={\displaystyle \sum _{\mathrm{i}=\mathrm{1}}^{\mathrm{n}}}\mathrm{\text{Wi}}*\mathrm{\text{Si}}$

Where Wi is weighed factor value of PC for the selected indicator. Si indicates that the scoring value of each selected indicator.

Statistical analysis

The SQI was assessed following Andrews et al. (2002) using SPSS 16.0 for PCA to find a MDS of key soil quality indicators. Data normality and suitability were verified through the Shapiro–Wilk, KMO, and Bartlett’s tests. Variables with eigenvalues >1 and high factor loadings were retained, and redundant ones removed. Indicators were scored using linear functions (“more is better” = X/Xmax; “less is better” = Xmin/X) and non-linear functions for “optimum is better” parameters (Bastida et al., 2006). The final SQI was derived using the WAI method as $SQI=\sum Wi\times Si$ , where Wi is the PCA-derived weight and Si the normalized indicator score.

Results

Fertilizer used during cropping season

Based on the information recorded on fertilizer addition, it showed that nitrogen was excessively used (35–225 kg ha⁻¹ than RDF) in almost 90% of the CF fields (Table 1). Regarding P, 40% of the CFs were added with lesser P (10–20 kg ha⁻¹) than RDF and remaining were supplemented with excess quantity (20–137 kg ha⁻¹) of P. For K, 30% of CFs were added with recommended K level and the remaining fields were excessively used with K (25–100 kg ha⁻¹). Micronutrient requirement was met through complex forms and micronutrient mixtures. The rice variety used in CFs was Samba Mashuri (BPT 5204) which is a long duration variety. Cropping pattern being followed in the organic field was rice-green manuring-rice- pulses for the first cropping season and rice cum pulses was in practice for the second cropping season. The manures applied were FYM @ 12.5 t ha⁻¹, rock phosphate @ 100 kg ha⁻¹ and neem cake at the rate of 200 kg ha⁻¹. Foliar application of panchagavya (@ 3% twice at maximum tillering and panicle initiation stage and also top dressing with vermicompost @ 1 t ha⁻¹ at active tillering and panicle initiation stage were being followed. The cropping pattern followed in INM field was rice-pulses-rice-green manure. The manures applied were FYM (12.5 t ha⁻¹) as basal dose during main field preparation, NPK applied was at the rate of 70:20:35 kg ha⁻¹. Micronutrients viz., zinc sulphate at the rate of 25 kg ha⁻¹ and micronutrient mixture was used.

Soil quality index computation for rice farming systems

Soil characterization

The soil pH recorded in selected fields ranged from (6.05) neutral to (8.20) slightly alkaline (Table 2). EC found to be non-saline and SOC in the medium range. The mean BD and WHC was 1.30 g cm⁻³ and 31.62% respectively. The available N content was low with overall mean value of 260 kg ha⁻¹. Available P and K were relatively sufficient. The overall mean available K content for four seasons was 286 kg ha⁻¹ with the maximum and minimum range of 248 and 319 kg ha⁻¹. The mean micronutrients range was found to be 1.67 mg kg⁻¹(Zn), 14.97 mg kg⁻¹ (Fe), 1.59 mg kg⁻¹ (Cu) and 5.47 mg kg⁻¹(Mn). The SMB-C in the rice fields was evaluated for four seasons and the maximum and minimum value recorded was 33.08 and 75.41 mg SMB-C 100 g⁻¹ soil (Table 2). The highest and lowest SMB-N recorded was 17.25 and 56.88 mg SMB-N 100 g⁻¹ soil. Variation among the samples was recorded with standard error (SE) and standard deviation (SD) of 1.50 and 10.66 mg SMB-N 100 g⁻¹ soil. The SMB-P varied from 3.02 to 10.63 mg SMB-P 100 g⁻¹ soil. The heterogeneity was minimum with standard deviation of 1.90 mg SMB-P 100 g⁻¹ soil. The UA ranged from 21.25 to 49.88 μg of urea hydrolyzed g⁻¹ h^-1. Marked variation was recorded among the soils with standard deviation of 6.51 μg of urea hydrolyzed g^-1 h^-1. The PA in the rice fields varied from 12.55 to 48.75 μg of PNP g^-1 h⁻¹ with SE and SD of 1.29–9.14 μg of PNP g⁻¹ h⁻¹. The DHA in rice soil under CFs varied from 7.29 to 31.44 μg TPF g⁻¹ 24 h⁻¹. The standard error and standard deviation recorded was 0.86 and 6.13 μg TPF g⁻¹ 24 h⁻¹ respectively (Table 2).

Table 2

Table 2. Descriptive Statistics for 10 conventional fields (n = 40).

Spatial and temporal variations in soil physical, chemical and biological characteristics

Conventional farming

As per the data recorded over four seasons under conventional fields, the bulk density (BD) of the soil ranged from 1.22 to 1.39 g cm⁻³, with an overall mean value of 1.30 g cm⁻³. The influence of fertilizer addition was not notably evident during the study. Regarding water-holding capacity (WHC), the percentage increase or decrease varied with soil texture. The silty loam and clay loam soils, viz., CF₁, CF₂, and CF₃, recorded the highest percentage decreases of 3.09%, 3.10%, and 2.33%, respectively, in soil WHC, whereas CF₄ (1.04%), CF₇ (2.12%), and CF₉ (0.32%)—with silty clay loam (CF₄ and CF₇) and loam (CF₉) textures—showed positive variations in WHC at the end of season four (S₄). Thus, no significant variation was observed due to fertilizer treatments. Detrimental effects of fertilizer addition were not observed in soil pH and EC at the end of the four seasons. Under CFs, the SOC content was slightly reduced, ranging from 0.15% to 0.36%, with the highest value recorded in CF₃ and the lowest in CF₅ and CF₉. A common strategy for enhancing soil organic carbon includes optimal fertilization combined with organic manure application; however, in CFs, irregular fertilization practices led to only nominal changes.

All the CFs were heavily applied with N fertilizers. The percentage increase in available N ranged between 3.83% and 11.50%, with the maximum and minimum observed in CF₄ and CF₁₀, respectively. In CF₂ (7.56%) and CF₈ (5.17%), a reduction in available N content was noted. Regarding P, 40% of the CFs received less P (10–20 kg ha⁻¹) than the recommended dose (RDF), while the remaining fields were supplemented with an excess quantity of P (20–137 kg ha⁻¹). A percentage increase in P content was observed in 60% of the CFs, ranging from 12.82% to 15.94%. The CFs that received less P than RDF showed a 6.61%–12.72% decrease in available P content. The addition of P led to an increase in available P content. With regard to K application, 30% of the CFs were applied with the recommended K level, whereas the remaining fields received K in excess (25–100 kg ha⁻¹). Regarding the percentage increase in available K content, 70% of the CFs recorded higher K. The DTPA-extractable micronutrient contents in the twelve rice fields showed minor variations over the four seasons. Zn ranged from 1.19 to 2.08 mg kg⁻¹, with a stable mean of 1.67 mg kg⁻¹ in S₄, half of the fields showing slight increases and half slight decreases (1.04%–2.48% increase; 1.57%–3.30% decrease). Fe varied between 12.06 and 16.68 mg kg⁻¹, with minimal changes in mean values (14.98 mg kg⁻¹ initially to 14.96 mg kg⁻¹ in S₄) and small increases or decreases across fields (0.12%–1.22% increase; 0.15%–1.60% decrease). Cu content ranged from 1.17 to 1.83 mg kg⁻¹, showing both upward and downward trends across seasons, with about 80% of fields recording a decline (maximum reduction in CF₅ and CF₇). Mn varied from 3.62 to 7.54 mg kg⁻¹, with a mean of 5.46 mg kg⁻¹ in S₄, half of the fields showing reductions (1.67%–3.25%) and the others slight increases (0.31%–3.15%), with the greatest and least variations observed in CF₈ and CF₁₀, respectively. Overall, micronutrient levels remained relatively stable, with only minor seasonal and field-to-field differences.

An insight into the SMB-C data revealed that 70% of the CFs was positively influenced by the fertilization. The highest percent increase in SMB-C was recorded in CF₆ (7.36 mg SMB-C 100 g⁻¹ soil) and CF₁₀ (7.45 mg SMB-C 100 g⁻¹ soil) meanwhile, the highest percent decrease in SMB-C was registered in CF₁ (13.32 mg SMB-C 100 g⁻¹ soil) and CF₇ (11.83 mg SMB-C 100 g⁻¹soil). Unlike organic carbon, the SMB-C quickly responded to added inputs but in mean time, strong association was noticed between SOC and SMB-C (r = 0.60^**) (Figure 4).

Among the CFs, the 50% of the fields showed reduction in SMB-N within the range of 0.04%–9.45% and left over fields registered the increase from 4.34% to 10.76%. The highest percent increase and decrease was registered in CF₁ (20.72–22.95 mg SMB-N 100 g⁻¹ soil) and CF₈ (23.5–21.28 mg SMB-N 100 g⁻¹ soil) respectively. Microbial N was positively correlated with SOC (r = 0.36^**) (Figure 4).

Excessive P fertilizer application favorably influenced the microbial P as they meet out the plant need during the active stages of growth. The SMB-P was positively correlated with available P (r = 0.25^**). Regarding percent increase or decrease, 20% of CFs exhibited the decrease within the range of 5.03%–6.30% and 80% of the CFs registered with 0.79%–8.85%.

As nitrogen was added in higher quantity (50%–150% excess than RDF) in CFs, the UA was found to be increased in 90% of the CFs. The percent increase was recorded from 0.34% to 8.74% and in CF₂ UA was reduced with percent decrease as 5.38%. PA activity is a good indicator of P mineralization potential and it was associated with SOC and it had positive correlation with SOC content (r = 0.50^**) (Figure 4). Marked variation in the PA was observed under the CFs within the range of 0.09%–12.27%. Among the CFs, 80% of the fields were positively influenced during the cropping season. In each field under CF system, change in DHA was noticed with the percentage variation from 3.79% to 7.38% increase and 2.48%–13.20% reduction. The highest and lowest percent increase (CF₁) and decrease (CF₃) was registered which seems to be not only by fertilizer input but also it connected with the organic matter and SOC content. Positive correlation between SOC and DHA was noticed with r value of 0.41^** (Figure 4).

Organic and INM farming system

Comparing the three system of farming, organic additions show positive effect on soil BD and WHC. In OF and INM based field, the initial pH was recorded as 7.73 and 6.72 respectively. At fourth season (S4), 2.46% and 0.89% percent reduction in comparison with initial soil pH was noticed in OF and INM field. Both increasing and decreasing trend in soil pH was noticed among the season so the overall changes in soil pH recorded during the four seasons were not in considerable level. From the above results, soil pH recorded in the study was in desirable range and drastic change was not seen during the four seasons of rice cropping. Higher C and N mineralization under organic manurial application raised the SOC after four cropping seasons (0.59%). Regarding Olsen–P, percent increase was registered as 14.5 ad 11.83% for OF and INM respectively. The added nutrients influenced the available K content with 7.30 ad 7.01% increase in OF and INM field respectively.

The initial SMB-C in the OF and INM field was recorded as 90.03 and 87.50 mg SMB-C 100 g⁻¹ respectively and at S4, the SMB-C in OF and INM was registered as 97.22 and 92.94 mg SMB-C 100 g⁻¹ of soil. Among the three systems, OF responded positively for the added inputs by changing the SMB-N content. The impact of added inputs was reflected by raising the microbial P content from 15.76 to 17.98 mg SMB-P 100 g⁻¹ soil and 12.88–14.63 mg SMB-P 100 g⁻¹ soil in OF and INM field respectively. Both the systems were differed in changing the microbial P, since the changes recorded in both the systems was unstable. Consistent pattern of increase in SMB-P was not noticed by the fertilizer addition because variation in the content of microbial P differed from season to season.

With respect to enzyme activity in the OF and INM fields, the initial UA content was recorded as 49.49 and 39.55 μg of urea hydrolyzed g⁻¹ h⁻¹. As in CFs, the UA activity was influenced by the added inputs. Stable increase in UA activity was noticed in OF field as it’s closely associated with organic matter content meanwhile the balanced addition of inorganic and organic inputs in INM influenced the urease activity in positive manner. The percent increase of 9.70% (OF) and 6.82% (INM) was recorded after the four cropping seasons (S4). Initial PA content recorded in the OF and INM field was 49.71 and 45.77 μg of PNP g⁻¹ h⁻¹ and notably the content was increased to 57.51 and 50.47 μg of PNP g⁻¹ h⁻¹. Computed increase in the DHA was 9.88% and 11.74% for OF and INM respectively.

Minimum data set (MDS) formulation for soil quality indicators

The soil quality for the selected rice fields was computed into an index value by using PCA for minimum data set indicators selection. The quantitative index values were derived by WAI method. Prior to PCA, the KMO measure and Bartlett’s test of sphericity were conducted to assess the adequacy of the data for factor analysis. The KMO value of 0.81 was obtained, indicated a meritorious level of sampling adequacy, suggesting that the correlation patterns were compact enough to yield distinct and reliable components. Bartlett’s test of sphericity was highly significant (χ² = 425.37, df = 136, p < 0.001), confirming sufficient interrelationships among the variables. These results validated the suitability of the dataset for PCA. The estimated soil quality index was correlated with crop yield for validation. The data in Table 3 indicated that highly weighed variables under PC1 included nitrogen and phosphatase. Under PC2, BD and WHC got higher loading factor and other variables did not get enough loading factor to qualify for MDS formation since their loadings did not fall within 10% of the highest weight. In the PC3, SMB-C got highest loading factor and in PC4, OC got highest loading factor. In PC 5, Zn received highest loading factor respectively and in PC6 urease was loaded with higher factor.

Table 3

Table 3. Principal components analysis results of eighteen (18) soil properties.

Soil quality index

The data presented in Tables 4, 5 revealed that relatively higher SQI values were observed in the OF, followed by INM, CF₆, and CF₃. Among the conventional systems (CFs), CF₁₀, CF₅, CF₁, and CF₄ recorded lower SQI values compared to other fields. The highest yield was recorded in INM, followed by CF₆ and CF₈. Among the various fields under CFs, the SQI ranged from medium to high, whereas in the OF system, a higher SQI was registered. A positive correlation was observed between SQI and yield (r = 0.73**) (Table 5).

Table 4

Table 4. Score, weight and soil quality index (SQI) values for selected minimum data set (MDS) variables for each field after the four-season crop harvest.

Table 5

Table 5. Soil quality index (SQI) and crop yield correlation for selected conventional, organic and INM field.

Soil quality index (SQI) interpretation for rice farming systems

Principal component analysis (PCA) and minimum data set indicators (MDS)

The soil quality index (SQI) for the selected rice fields was computed using PCA for MDS selection (Table 5). The first six PCs explained a substantial portion of the total variance: PC1 (22.73%), PC2 (20.45%), PC3 (17.34%), PC4 (10.98%), PC5 (7.37%), and PC6 (6.31%). PCs with higher variance contributions (PC1–PC3) can be considered most informative for understanding soil and crop dynamics (Figure 2). The SQI was validated against crop yield (Table 5). Key variables with high loadings included N and phosphatase in PC1, bulk density (BD) and water-holding capacity (WHC) in PC2, SMB-C in PC3, OC in PC4, Zn in PC5, and UA in PC6. For MDS formation, PA, WHC, SMB-C, OC, Zn, and UA were retained, as they represented the most influential indicators in each principal component. These results indicate that soil chemical, physical, and biological properties collectively influence soil quality, which in turn is closely associated with crop yield variations across the studied fields.

Figure 2

Figure 2. PCA loading plots illustrating the relationships among parameters. Left: PC1 vs. PC2, accounting for 43.18% of the cumulative variance. Right: PC1 vs. PC3, accounting for 60.52% of the cumulative variance. PCs with higher variance contributions (PC1–PC3) can be considered most informative for understanding soil and crop dynamics.

Soil quality index

SQI is a product of selected soil indicator which has dominant influence on soil quality. From the PCA, the highest loading factor in PC1, PC3, PC4, PC6 were for biological properties and they can be phrased as biological components (Table 3). In PC2, the factor loading was higher for WHC and it can be termed as physical component (Table 3).

The SQI values recorded for OF (0.99) and INM (0.88) systems were higher compared to conventional farming fields (Table 5). Marked variation was observed among the CFs, with the highest SQI recorded in CF₆ and CF₃. Nitrogen was excessively applied in all the CFs, except CF₂, and the highest levels of N supplied to the CFs followed the order: CF₄ > CF₉ > CF₃ > CF₅. The corresponding yields in these fields were 3.20, 4.01, 4.56, and 3.39 t ha⁻¹, respectively. A positive correlation was recorded between SQI and yield (r = 0.73**) (Figure 3).

Figure 3

Figure 3. The scatter plot showing the relationship between Soil Quality Index (SQI) and average crop yield (t ha⁻¹). The red dashed line represents the linear regression fit with R² = 0.73, indicating a strong positive relationship.

CF₄, which received a super-optimal dose of N (250%), showed lower SQI (0.573) as well as reduced yield (3.20 t ha⁻¹), whereas CF₆, supplied with 125%N, recorded the highest SQI (0.715) and yield (6.20 t ha⁻¹) (Table 5). Likewise, most CF fields receiving N beyond 125%, except CF₃ and CF₉, exhibited lower yields (Table 5). Forty percent of the CFs received excess phosphorus, while all CFs were supplied with super-optimal potassium. Fields CF₆, CF₇, CF₁, and CF₁₀ that received super-optimal phosphorus generally exhibited reduced yields, with the exception of CF₆. Similarly, CF₄, CF₇, and CF₉, which received super-optimal K application, also showed lower yields. These results indicate that applying NPK beyond optimal levels can decrease both crop yield and soil quality. Notably, the soil treated with 125%N + 150%P + 200%K (CF₆) achieved the highest SQI and yield (Table 5).

Discussion

Soil characteristics under conventional, organic and INM farming systems

Conventional farming fertilization practices on soil quality

Paddy soils are generally not homogeneous since much complex relationships exist between physical and chemical characteristics of soil (Duan et al., 2020). Among that BD is important to meet out the hydrological requirement of plant (Akram et al., 2018). As per the data recorded for four seasons under conventional fields, variation in BD after the four seasons of cropping was not found. The soil water regime assists with soil texture and structure (Wang L. et al., 2023; Smirnova and Kozlov, 2023). The present study showed no significant influence of NPK addition on WHC, indicating that only organic amendments can assist in bringing about measurable changes. Irrespective of soil texture, continuous paddy cultivation is concerned for increased BD (Uddin et al., 2022; Abe et al., 2022).

Soil pH in paddy soils is dynamic; following flooding, it tends to shift toward neutrality and returns to normal levels after cycles of wetting and drying (Rupngam and Messiga, 2024). Changes in pH strongly influence the soil’s net negative surface charge, thereby affecting its affinity for metal ions (Naz et al., 2022; Aggarwal et al., 2025). Over the four seasons, a slight reduction in soil pH was observed across all the CFs and it was observed that fields receiving excess nitrogen generally showed a slight reduction in soil pH; however, in CF₆ and CF₈, a marginal increase in pH was noted despite higher N application, which could be attributed to the buffering capacity and inherent calcareous nature of the soil (Zhang et al., 2017). Thus chemical fertilization alone won’t change the soil pH level but the nature of the soil also presumably decides (Pahalvi et al., 2021). The excess P added soils increased the EC level but found to be in safe range (1.0 dS m⁻¹). The P fertilizers have the tendency to increase the soil EC than N and K (Boukhalfa-Deraoui et al., 2015).

In agricultural management, SOC is a key factor determining the nutrient-supplying capacity of the soil (Sarma et al., 2024). Under CF fields, SOC content was only slightly reduced, ranging from 0.15% to 0.36%, with the highest value observed in CF₃ and the lowest in CF₅ and CF₉. The minor reduction in SOC may be attributed to continuous tillage, removal of crop residues, and imbalanced or excessive fertilizer application, which can accelerate organic matter decomposition and limit carbon accumulation. While common strategies for enhancing SOC include optimal fertilization and incorporation of organic amendments, irregular fertilization practices in the CFs likely led to these minimal changes. These observations are consistent with the findings of Bhavani et al. (2017).

Consequently, to improve the crop production, N addition is indispensable and unavoidable. Judicious application of N guaranteed for the utmost productivity with superior crop quality (Ali et al., 2025; Boruah et al., 2023). In the study, different fertilization levels had a positive effect on soil available N. All the CFs were enormously applied with N fertilizers. However, the reflection of added N levels was registered after the four seasons. The percent increase ranged between 3.83% and 11.50% and the maximum and minimum was noticed in CF₄ and CF₁₀. In the CF₂ (7.56%) and CF₈ (5.17%) reduction in N content was noted. A higher dosage of chemical nitrogen increased the available N content, whereas a decline was observed in soils receiving lower N additions. It is also confirmed by Li et al. (2022). Available N was positively associated with SOC(r = 0.52**), SMB-C (r = 0.42**), SMB-N (r = 0.85**) and PA (r = 0.83**).

P pools change frequently by the application of different organic and inorganic fertilizers. Soil chemical nature is possibly affected by these inputs and changes take place in the availability of nutrients and its distribution pattern (Williams et al., 2014; Liu et al., 2024). Previous studies mentioned that, long term imbalanced fertilization change the total soil phosphorus and available P stocks. Besides, P pools (Total P, inorganic P and organic P) were majorly decided by the chemical nature of the soil. P fertilizer application had a less influence on the residual and organic P fractions but greatly affect the soluble inorganic P (Zhang C. et al., 2023). The addition of excess P in CFs manifested the effect of increase in available P content. The increase in olsen-P content not only decided by the chemical fertilization but also by soil pH and organic matter (Battisti et al., 2022). Because soil pH chiefly decides the release of the mineral associated P and organic matter reduce P adsorption by the releasing the organic anions to compete for the adsorption sites and mean time low molecular weight organic acids dissolve the mineral associated P (Chen et al., 2022).

Under intensive cultivation, crops readily remove exchangeable and easily available K. Since potassium exists in a dynamic equilibrium in the soil, it must be maintained at balanced levels (Das et al., 2022). In such conditions, non-exchangeable K contributes by releasing potassium into the soil solution and exchangeable pool. Thus, soil K dynamics are strongly influenced by the rate of fertilizer application (Dong et al., 2022; Andrews et al., 2021). Consistently, available K in the soil increased with higher levels of K fertilization.

The most active and dynamic pool of SOM is microbial biomass carbon and it act as a temporary nutrient storage source accountable for the nutrient release to plants (Khan et al., 2025). An insight into the data revealed that 60% of the CFs was positively influenced by the fertilization.

Unlike SOC, the SMB-C quickly responded to added inputs but, strong association was noticed between SOC and SMB-C (r = 0.60**). Microbial N was positively correlated with SOC content (r = 0.36**) and available N (r = 0.85**) (Figure 4). Low N added soil (CF₂) show reduction in SMB-N and in the remaining CFs higher N addition increased the SMB-N except CF_4, CF_7, CF₈ and CF_9. Because not only the N fertilization but also the SOC and available N content also aid in the enhancement of the SMB-N (Lasar et al., 2025). The highest percent increase was recorded in CF₇ and CF₆ and this could be possibly due to excessive P fertilizer application favorably influenced the microbial P as they meet out the plant need during the active stages of growth. Not only P fertilizer application but also the initial soil available P content and SOC content might have helped for higher SMB-P (Wang et al., 2025; Malik et al., 2013). In the study, SMB-P was positively correlated with available P (r = 25**) (Figure 4).

Figure 4

Figure 4. Heatmap showing Pearson’s correlation coefficients among soil physical, chemical, and biological parameters in rice farming systems. Positive correlations are represented by red shades, while negative correlations are shown in blue. The intensity of the color indicates the strength of the relationship (|r|). Significant correlations at p < 0.01 and p < 0.05 highlight interactions among nutrient availability and microbial activities.

Excess N application increased UA (Adetunji et al., 2017). UA activity was influenced not only by urea addition but also by SOC (r = 0.43*) (Adetunji et al., 2017). Phosphorus addition promotes root proliferation, which can enhance enzyme activity, while nitrogen supply further supports PA by facilitating P mobilization. Variation in PA was recorded across the four seasons, with marked differences observed among the CFs, ranging from 0.09% to 12.27%. Among the CFs, 70% of the fields showed positive influence during the cropping season. In the present study, soils receiving low P exhibited reduced PA (2.19%–7.31%), whereas soils with high NP application showed enhanced PA. Bhavani et al. (2017) similarly reported that increasing NPK levels to 150% enhanced PA in rice at the flowering stage. In addition to SOC content, soil type also influenced PA (Zheng et al., 2018). Since PA is a reliable indicator of P mineralization potential and is associated with SOC, a positive correlation with SOC content was observed (r = 0.50*) (Figure 4).

DHA activity is a reliable indicator of soil microbial activity, as it is active only within living cells, unlike many other enzymes. DHA activity is influenced by both organic and inorganic fertilization (Kumar A. et al., 2025; Parihar et al., 2025). Phosphorus-deficient fertilization significantly reduced soil DHA compared to plots receiving adequate P (Yuan et al., 2025; Velmourougane et al., 2013). In conventional fields (CFs), DHA showed variations, with percentage increases ranging from 3.79% to 7.38% and reductions from 2.48% to 13.20%. The highest increase was observed in CF₁, and the greatest decrease in CF₃, indicating that DHA is influenced not only by fertilizer inputs but also by organic matter and SOC content. This is supported by a positive correlation between SOC and DHA (r = 0.41**) (Figure 4). Fields receiving low P exhibited a decline in DHA, whereas excess N application generally enhanced DHA. However, soils receiving both excess N and low P showed decreased DHA. A temporary increase in DHA following urea application was also reported by Filipek-Mazur et al. (2025).

Organic and INM practices on soil quality

Organic and INM practices are less widely adopted in the study region, primarily due to factors such as limited awareness, higher labor requirements, and the gradual transition from conventional farming practices. Consequently, only one field each under OF and INM management could be identified and included in this study. These fields were selected to represent the typical management approaches for these systems in the region, providing initial insights into their effects on soil chemical and biological properties despite the limited replication.

Comparing the three system of farming, organic additions show positive effect on soil BD (Murtaza et al., 2025). Soil organic matter reduction invariably reduces porosity and increase the bulk density. Hence it is vital to include organic matter additions which could aid in aggregate formation in balancing the bulk density of soil (Sadiq et al., 2025; Bashir et al., 2021). Application of organic manures significantly increased the WHC of the soil than INM field. The binding agents released during the decomposition of added manures helped in the formation of stable soil aggregates, which improved soil structure, enhanced porosity, and facilitated better water retention (Sarker et al., 2022; Malik et al., 2013; Malik et al., 2014).

In the OF and INM-based fields, the initial soil pH was recorded as 6.72 and 7.73, respectively. By the fourth season (S₄), a 2.46% reduction in soil pH was observed in the OF field, whereas no appreciable change was noted in the INM field. Both increasing and decreasing trends in soil pH were observed across the seasons; however, the overall changes during the four rice-cropping seasons were minimal. These results indicate that the soil pH remained within a desirable range, with no drastic fluctuations. The slight decline in pH suggests that inorganic and organic fertilization may help mitigate soil acidification to some extent (Tao et al., 2019; Raza et al., 2020). Meanwhile, the addition of organic manure can also generate soil acidity due to organic matter decomposition. Initially, organic matter releases cations and anions, which may temporarily increase soil pH. Subsequently, microbial breakdown of added manure into ammonium further raises pH, but the conversion of ammonium to nitrate gradually lowers pH over time, potentially causing greater acidification under long-term application (Wang et al., 2020; Sun et al., 2023).

The organic additions under OF and INM field reduced the soil EC level. This could be due to the effect of the organic metal ions and the acidifying effect in the rhizosphere region (Boukhalfa-Deraoui et al., 2015; Bhatt M. K. et al., 2019). The variation recorded after four seasons was 0.59% and 0.14% increase in OF and INM field respectively. Higher C and N mineralization under organic manurial application resulted in such variation. The continuous flooding under rice cultivation would increase the SOC. Addition of organics coupled with addition of root biomass would have helped maintaining SOC (Yan et al., 2025; Kumari et al., 2024). The percent increase in the SOC was 6.25% and 5.46% in OF and INM field respectively. Organic addition helps to maintain the available N content as well as the SOC (Singh Brar et al., 2015; Beura et al., 2018; Singh et al., 2018). The percent increase in olsen-P was lower in OF and INM than CFs. This may be due to immobilization of P by microbes as evidenced in recording higher SMB-P in the study. The SMB-P recorded in OF and INM field was 14.08 and 13.59 mg SMB-P 100 g^-1 soil respectively. Similar reports were given by Pant et al. (2024). The added nutrients influenced the available K content with 7.30 ad 7.01% increase in OF and INM field respectively. Rich source of FYM addition at the rate of 12.5 t ha^-1 could have contributed to such K build up in soil (Bolo et al., 2024; Dhaliwal et al., 2023).

Unlike SOC, the SMB-C quickly responded to added inputs but, strong association was noticed between organic carbon and SMB-C (r = 0.60**). The increase in SMB-C was found to be higher in OF than INM and conventional farming. Complete organic additions act as a stable source to organic carbon in order to support the microbial activity than INM and CF (Zheng et al., 2018; Gupta et al., 2019).

Among the three systems, OF field responded positively for the added inputs by changing the SMB-N content. The impact of added inputs was reflected by raising the microbial P content from 15.76 to 17.49 mg SMB-P 100 g⁻¹ soil and 12.88–14.63 mg SMB-P 100 g⁻¹ soil in OF and INM field respectively. Both the systems were differed in changing the microbial P, since the changes recorded in both the systems was unstable. But organic fertilizers acted as an imperative role in increasing the biomass than conventional fertilization (Abebe et al., 2022).

Stable increase in UA was noticed in OF field as it’s closely associated with organic matter content and the balanced addition of inorganic and organic inputs in INM influenced the UA activity in positive manner (Adetunji et al., 2017; Choudhary et al., 2021). Soil PA was enhanced by organic additions and marked increase in the enzyme activity after the organic additions might be due to decomposition of organic amendments release or trigger a molecule which give intimations to the soil organisms to secrete more phosphatase enzymes (Nannipieri et al., 2018). Organic additions favour for DHA and it was inclined by the quality of the organic matter not by quantity added to soil (Kumar D. et al., 2025). As both OF and INM farming received green manures which might have promoted the DHA in soil. Similar results reported by Nguyen Do Chau et al. (2024) who found increased DHA with green manure as compared to coirpith, FYM and paddy straw.

Soil quality index (SQI) and indicators

Monocropping of rice combined with imbalanced fertilization practices has distinct effects on soil properties. In many previous studies, the selection of evaluation indicators was often based on researchers’ experience rather than statistical approaches, limiting the identification of the most representative parameters characterizing soil quality. Long-term fertilization under rice monocropping has been reported to negatively affect soil properties (Li et al., 2021). In the present study, soil quality assessment incorporated not only chemical indicators but also physical and biological parameters, enabling a more comprehensive understanding of the relationship between soil quality and yield under rice monocropping systems. Based on PCA results, six indicators - PA, WHC, SMB-C, OC, DTPA-Zn, and UA were identified with high weighting factors for inclusion in the MDS to evaluate the selected farming systems. Consistent with previous research, SOC and SMB-C were considered key soil quality indicators (Peng et al., 2020; Yu et al., 2018).

The highest SQI observed in the OF field (0.99), followed by INM (0.88), reflects the positive impact of organic amendments on soil quality. Organic inputs enhance soil organic matter, improving nutrient availability, aggregate stability, and water-holding capacity. They also promote stable aggregate formation, reduce bulk density, and increase porosity, which facilitates root growth and microbial activity. Enhanced microbial activity under organic management further supports nutrient cycling and enzymatic processes, contributing to soil biological health (Bhowmik et al., 2024; Chen et al., 2025). In addition, slow nutrient release and improved pH and moisture buffering maintain soil quality and resilience. These structural, chemical, and biological improvements collectively explain the superior SQI in organic fields, confirming that organic management practices effectively sustain and enhance soil quality, in agreement with previous studies. However, the yield in the organic field was lower than in the INM fields, possibly because nutrient requirements of the crop were not fully met due to the slow nutrient release from organic matter through mineralization (Werner et al., 2023). Despite the OF field exhibiting the highest SQI (0.99), its yield was lower than that of INM and conventional fields. One primary reason is the slower nutrient release from organic amendments such as FYM and panchakava, which limits the availability of nitrogen, phosphorus, and potassium during critical stages of crop growth (Mondal et al., 2016; Kumar et al., 2023). Nitrogen, in particular, is often a limiting factor in organic systems, as mineralization rates may not match crop demand (Geisseler et al., 2021; Valenzuela, 2023). Additionally, organic systems typically avoid synthetic pesticides, which can lead to higher pest and disease pressure, further reducing yields (Benbrook et al., 2021). Nutrient imbalances, temporary nutrient immobilization due to microbial activity, and water management challenges can also constrain productivity (Zhang Y. et al., 2022). Overall, while organic practices enhance soil quality and sustainability over the long term, short-term yields often lag behind those of conventional or integrated nutrient management systems (Paramesh et al., 2023).

Among the conventional fields, marked variations in SQI were observed, with the highest values recorded for CF₆ and CF₃. Nitrogen application was excessive in most CFs, except CF₂, with the highest N inputs applied in the order CF₄ > CF₉ > CF₃ > CF₅. Corresponding yields in these fields were 3.20, 4.01, 4.56, and 3.39 t ha⁻¹, respectively. A significant positive correlation was observed between SQI and yield (r = 0.73*), highlighting the influence of soil quality and productivity.

The CF₄ received with super-optimal dose of N (250%) showed lower SQI value (0.573) as well as the yield (3.20 t ha⁻¹) and the CF₆ which received 125% N recorded the highest SQI (0.715) and yield (6.20 t ha⁻¹) underscore the non-linear response of soil quality to nutrient inputs. Excess nitrogen fertilization can accelerate soil acidification and lead to leaching of base cations, thereby degrading soil chemical balance and microbial activity, which in turn reduces biological indicators central to SQI (Hui et al., 2022). Likewise, most of the conventional fields which received higher N beyond the level of 125% except CF₃ and CF₉ recorded lower yield. This decline in productivity at higher nitrogen levels may be attributed to nutrient imbalance, soil acidification, and reduced nitrogen-use efficiency caused by excessive N inputs (Govidasamy et al., 2023; Whetton et al., 2022). Overapplication of nitrogen often leads to luxury consumption by plants, delayed maturity, and lodging, ultimately reducing harvestable yield (Liu et al., 2025; Wang P. et al., 2023). Forty percent of CFs were added with excess P and all the CFs were supplied with excess K. Most fields receiving excess phosphorus (CF₆, CF₇, CF₁ and CF₁₀) recorded lower yields, except CF₆. Excess P application likely caused nutrient imbalance and reduced P-use efficiency due to fixation and inhibition of micronutrient uptake (Zhang J. et al., 2023; Wang et al., 2024). The relatively better yield in CF₆ might be attributed to favorable soil properties or microbial activity enhancing P availability (Li et al., 2025). Excess potassium application also resulted in reduced yields in CF₄, CF₇, and CF₉. This showed that, increasing the level of NPK to super-optimal level would reduce the yield as well as the soil quality. High K levels may antagonize the uptake of calcium and magnesium, leading to impaired enzyme activity and photosynthetic efficiency (Zörb et al., 2014). Moreover, continuous application of NPK beyond the optimal threshold can decrease soil cation exchange capacity, alter microbial activity, and accelerate soil quality decline (Wang et al., 2024; Li et al., 2025). These results confirm that excessive nutrient inputs, instead of enhancing productivity, can reduce yield and degrade soil quality (Hui et al., 2022; Sun et al., 2020; Pei et al., 2024; Isobe et al., 2018).

Basak et al. (2016) reported that among three soil orders (Alfisol, Entisol, and Inceptisol), organic carbon for Entisols, labile carbon for Alfisols, and DHA activity and cation exchange capacity for Inceptisols were strongly correlated with system yield, indicating the sensitivity of these parameters. Similarly, Rakshit et al. (2015) reported SQI values of 0.59–0.63 under integrated nutrient management (INM) in a rice–wheat system. The slightly lower and narrower range compared to this study’s 0.573–0.99 may be attributed to differences in soil properties, cropping systems, and the specific indicators used to calculate SQI. Bhattacharyya et al. (2019) also demonstrated that inclusion of biological indicators such as enzyme activity and microbial biomass substantially improved SQI prediction accuracy under long-term fertilization. In another study, Biswas et al. (2017) and Datta et al. (2021) emphasized that multivariate selection of indicators such as SOC, urease, and dehydrogenase enhanced the sensitivity and reliability of soil quality models. Aravindh et al. (2020) developed a biological quality index that incorporated microbial functional diversity and enzyme dynamics, validating the robustness of enzyme-based indicators in tropical soils. Kumar et al. (2022) carried out a long-term assessment under four rice-based cropping systems (rice–wheat, rice–chickpea, rice–lathyrus, rice–fallow) and two major soil orders (Vertisols, Inceptisols) in the hot sub-humid eco-region of India. They used a minimum data set (MDS) of 24 soil attributes screened via principal component analysis (PCA) and identified the most sensitive indicators, in descending order: field capacity (35.4%), pH (30.5%), porosity (13.6%), potentially mineralisable carbon (11.8%) and available boron (8.7%). Their soil quality index (SQI) values were highest in Vertisols (0.83) compared to Inceptisols (0.73), and among cropping systems SQI ranked: rice–chickpea (0.86) > rice–lathyrus (0.81) > rice–wheat (0.76) > rice–fallow (0.78). Aravindh et al. (2020) developed a dedicated biological quality index emphasising enzyme activity and microbial biomass for semi-arid soils; a 2023 study on tree-based land-use systems applied non-linear weighted SQI scoring and found dehydrogenase and acid-phosphatase among the most responsive indicators; and Shah et al. (2022) under integrated nutrient management in Himalayan foothills reported water-holding capacity, dehydrogenase activity and total bacterial count as major contributors to SQI improvements. Together these studies substantiate that combining chemical (OC, Zn, WHC) and biological (SMB-C, phosphatase, ase) indicators, and using robust indicator-selection (PCA/MDS) and scoring functions (non-linear) enhances the reliability of soil-quality assessment, supporting the present findings. Collectively, these studies demonstrate that chemical and biological properties such as PA, WHC, SMB-C, SOC, Zn, and UA are highly sensitive and have a major impact on soil quality.

Conclusion

The study demonstrates that soil management practices under CF, OF and INM influence soil quality and crop productivity. OF and INM fields exhibited higher soil quality indices (0.99 and 0.88, respectively) compared to conventional farming, which recorded lower values. Excessive application of N, P, and K beyond optimal levels reduced both soil quality and yield, as observed in fields receiving super-optimal doses. Evaluation of soil physical, chemical, and biological properties using the WAI method effectively captured the relationship between soil quality and function. These findings emphasize that adopting optimal fertilizer doses, combined with organic amendments, can enhance soil quality and sustain productivity in rice cropping systems.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

KT: Software, Investigation, Writing – review and editing, Methodology, Data curation, Project administration, Conceptualization, Supervision, Writing – original draft, Formal Analysis. SV: Investigation, Writing – review and editing, Software. RM: Formal Analysis, Writing – review and editing. VR: Writing – review and editing, Methodology, Formal Analysis.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgements

We thank the Vellore Institute of Technology for ongoing support in our efforts to carry out this research.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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